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Documentation Index

Fetch the complete documentation index at: https://docs.universalbench.dev/llms.txt

Use this file to discover all available pages before exploring further.

The obvious use of UniversalBench is from an AI coding assistant. But the platform is just as useful from places people often forget MCP works.

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Where UniversalBench is also used

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Slack and Discord bots

Wire your team chatbot to UniversalBench. Now /deploy runs safe_deploy. /incident opens a parallel investigation across logs, deploys, and on call. /secrets list checks the vault.

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Long running agents

Background workers, polling systems, supervisor agents. Anything that runs without a human in the loop. UB’s session_id keeps state across days of execution.

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Cron jobs and webhooks

A webhook receives an event, has Python decide what to do via UB’s workbench_execute, optionally reads and writes via db_select and _db_upsert, all without a single line of integration glue.

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Personal automation

You do not need a frontend to use AI. A Python script with the MCP SDK plus a single prompt template gives you a personal “ask anything” agent that knows your databases, your repos, and your secrets.

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Customer facing AI features

Your SaaS has an AI assistant for end users. UniversalBench lets it actually do things in your customer’s account: query their data, run their integrations, deploy their config changes. With per customer secrets, it stays multi tenant safe.

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Architectural patterns

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Pattern 1: AI as controller

The AI is the orchestrator. UB is its execution arm. The AI decides what to do, UB does it. 
User -> AI -> UB -> (DB, GitHub, web, code) -> AI -> User
Use when tasks are heterogeneous and decisions matter.

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Pattern 2: AI as translator

The user describes intent in natural language. The AI translates to UB tool calls. The AI summarizes results back. 
"Show me yesterday's signups" -> AI -> db_select -> AI -> "12 signups, mostly from..."
Use when replacing a UI for read only or simple write workflows.

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Pattern 3: AI as judge

The AI evaluates UB’s outputs against criteria you define. It loops until criteria are met. 
User -> AI -> safe_deploy -> smoke test fails -> AI fixes -> safe_deploy -> ...
Use when you want automated correction loops.

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What is next

Once you have used UB in one context, the rest of these are quick to spin up. The hardest part is realizing where AI plus execution unlocks something new for your team. Reach out at hi@universalbench.dev if you are building something interesting. We collect case studies and the best ones get featured.